Binding Analysis for Regulation of Transcription

About BART

BART (Binding Analysis for Regulation of Transcription) is a bioinformatics tool for predicting functional transcription factors (TFs) that bind at genomic cis-regulatory regions to regulate gene expression in the human or mouse genomes, given a query gene set or a ChIP-seq dataset as input. BART leverages 3,485 human TF binding profiles and 3,055 mouse TF binding profiles from the public domain (collected in Cistrome Data Browser) to make the prediction.

BART is implemented in Python and distributed as an open-source package along with necessary data libraries.
BART package is also available on Github.
BART web interface (Beta version) is available here.

BART is developed and maintained by the Chongzhi Zang Lab at the University of Virginia.

Installation

Prerequisites

BART uses Python's distutils tools for source installation. Before installing BART, please make sure either Python2 (Python2.7 or higher is recommended) or Python3 (Python 3.3 or higher is recommended) is installed in the system, and the following python packages are installed:

To install a source distribution of BART, unpack the distribution tarball and open up a command terminal. Go to the directory where you unpacked BART, run the install script to install BART globally or locally. For example, if you want to install the package BART-v1.0.1-py3-full.tar.gz:

You can download the Human or Mouse Data Library separately under your own directory. In this case, you have to edit the config file (e.g., BART1.0.1/BART/bart.conf) after you unpack the source package to provide the directory for the data. For example, if you download the hg38_library.tar.gz (or mm10_library.tar.gz) and unpack it under /path/to/library, then you can modify the bart.conf file as:

hg38_library_dir = /path/to/library/

Then you can run the install script and install BART source package globally or locally same as the full package described above.

Target transcription factors of interests, please put
each TF in one line. BART will generate extra plots
showing prediction results for each TF.

-p <processes>, --processes <processes>

Number of CPUs BART can use.

--nonorm

Whether or not do the standardization for each TF by
all of its Wilcoxon statistic scores in our compendium. If
set, BART will not do the normalization. Default:
FALSE.

Output arguments:

--outdir <outdir>

If specified, all output files will be written to that
directory. Default: the current working directory

-o <ofilename>, --ofilename <ofilename>

Name string of output files. Default: the base name of
input file.

Notes:
The input file for bart profile should be
BED or BAM format in either hg38 or mm10.

Bed is a tab-delimited text file that defines the data lines, and the BED file format is described on UCSC genome browser website. For BED format input, the first three columns should be chrom, chromStart, chromEnd, and the 6th column of strand information is required by BART.

name_auc.txt contains the ROC-AUC scores for all TF datasets in human/mouse, we use this score to measure the similarity of TF dataset to cis-regulatory profile, and all TFs are ranked decreasingly by scores. The file should be like this:

name_bart_results.txt is a ranking list of all TFs, which includes the Wilcoxon statistic score, Wilcoxon p value, standard Wilcoxon statistic score (zscore), maximum ROC-AUC score and rank score (relative rank of z score, p value and max auc) for each TF. The most functional TFs of input data are ranked first. The file should be like this:

name_plot is a folder which contains all the extra plots for the TFs listed in target files (target.txt file in test data). For each TF, we have boxplot, which shows the rank position of this TF in all TFs (derived from the rank score in name_bart_results.txt), and the cumulative distribution plot, which compares the distribution of ROC-AUC scores from datasets of this TF and the scores of all datasets (derived from the AUC scores in name_auc.txt).